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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

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Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
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Quantitative Aspects of Drug-Receptor Interaction01:30

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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The Two-State Receptor Model01:29

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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Identification of Kinase-substrate Pairs Using High Throughput Screening
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Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.

Sandeepkumar Kothiwale1, Corina Borza2, Ambra Pozzi3,4

  • 1Department of Chemistry, Center for Structural Biology, Institute of Chemical Biology Vanderbilt University, Nashville, TN 37232, USA. kothiwale.sandeep@gmail.com.

Molecules (Basel, Switzerland)
|September 20, 2017
PubMed
Summary

Developing selective kinase inhibitors is crucial for drug discovery. This study introduces a computational model to predict inhibitor activity against 379 kinases, aiding early-stage drug development and improving compound selectivity profiling.

Keywords:
">heminfoBCL::Cartificial neural networkskinase selectivity profilequantitative structure–activity relation

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Area of Science:

  • Medicinal Chemistry
  • Computational Biology
  • Pharmacology

Background:

  • Selective inhibitors of biological targets are key in drug discovery, but challenging for kinase enzymes due to conserved ATP binding pockets.
  • Profiling lead compounds against the large human kinome (around 500 targets) for selectivity is difficult and can change during optimization.

Purpose of the Study:

  • To introduce a computational model for early-stage profiling of kinase inhibitors.
  • To predict the activity and selectivity of compounds against a broad panel of kinases.

Main Methods:

  • Developed a quantitative structure-activity relation (QSAR) model using artificial neural networks.
  • Trained the model on activity data of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases.
  • Evaluated model performance using the area under the curve (AUC) of receiver operating characteristic (ROC) curves.

Main Results:

  • The QSAR model predicts kinase inhibitor activity with AUC values ranging from 0.6 to 0.8, depending on the specific kinase.
  • The model supports early profiling of compounds, aiding in the identification of selective inhibitors.

Conclusions:

  • The developed computational model facilitates early-stage drug discovery by predicting kinase inhibitor activity and selectivity.
  • This tool assists researchers in navigating the complexities of kinase inhibitor profiling and optimizing lead compounds.